A Hermite polynomial algorithm for detection of lesions in lymphoma images

Pattern Analysis and Applications - There are different types of lesions that can be investigated with the hematoxylin–eosin staining protocol. Lymphoma is a type of malignant disease which...     

Optimization framework for DFG-based automated process discovery approaches

The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

     

Synthesis and Evaluation of 2-Aminothiophene Derivatives as Staphylococcus aureus Efflux Pump Inhibitors

2-aminothiophene derivatives (2AT) in which the thiophene ring is fused with a cycloalkyl or a N-acylated piperidine ring by positions 5 and 6 and carrying a 3-carbethoxy group were synthesized and their bacterial growth and enzyme inhibitory effects against efflux proteins of Staphylococcus aureus leading to resistance to fluoroquinolones and erythromycin (ERY) were investigated. Compounds that most effectively decreases the minimum inhibitory concentrations (MICs) of ciprofloxacin (CIP) were assayed for their dose and time effects on the accumulation and efflux of ethidium bromide (EtBr) in the SA-1 strain. None of the compounds displayed antibacterial activity however, three derivatives carrying 2-amino, 2-aminoacetyl and 2-aminotrifluoroacetyl group enhanced the activity of CIP and ERY by 8- and 16-fold, respectively, and were able to restore the sensitivity of resistant strains, acting as typical efflux pump inhibitors (EPIs). The 2-aminoacetyl and 2-aminotrifluoroacetyl derivatives and two other piperidinyl 2-aminotrifluoroacetyl derivatives increased EtBr accumulation in a dose- and time-dependent manner, and one of them was also able to inhibit the EtBr efflux. Taken together, these results represent an important advance in the development of new EPIs, and demonstrate that 2AT represent a good scaffold for developing new antibiotic adjuvants.     

An automatic fire detection system based on deep convolutional neural networks for low-power,resource-constrained devices

Neural Computing and Applications - Large-scale fires have been increasingly reported in the news media. These events can cause a variety of irreversible damage, what encourages the search for...     

The future of functional food: Emerging technologies application on prebiotics,probiotics and postbiotics

This review was the first to gather literature about the effect of emerging technologies on probiotic, prebiotic, and postbiotic products. Applying emerging technologies to probiotic products can increase probiotic survival and improve probiotic properties (cholesterol attachment, adhesion to Caco-2 cells, increase angiotensin-converting enzyme (ACE) inhibitory, antioxidant, and antimicrobial activities, and decrease systolic blood pressure). Furthermore, it can optimize the fermentation process, produce or maintain compounds of interest (bacteriocin, oligosaccharides, peptides, phenolic compounds, flavonoids), improve bioactivity (vitamin, aglycones, calcium), and sensory characteristics. Applying emerging technologies to prebiotic products did not result in prebiotic degradation. Still, it contributed to higher concentrations of bioactive compounds (citric and ascorbic acids, anthocyanin, polyphenols, flavonoids) and health properties (antioxidant activity and inhibition of ACE, α-amylase, and α-glucosidase). Emerging technologies may also be applied to obtain postbiotics with increased health effects. In this way, current studies suggest that emerging food processing technologies enhance the efficiency of probiotics and prebiotics in food. The information provided may help food industries to choose a more suitable technology to process their products and provide a basis for the most used process parameters. Furthermore, the current gaps are discussed. Emerging technologies may be used to process food products resulting in increased probiotic functionality, prebiotic stability, and higher concentrations of bioactive compounds. In addition, they can be used to obtain postbiotic products with improved health effects compared to the conventional heat treatment.     

Mechanical and dimensional performance of poly(lactic acid) 3D-printed parts using thin plate spline interpolation

In additive manufacturing, determining the correct deposition parameters is very important as this can affect the final properties of printed parts. Since there is no agreement on the optimal level of the different printing parameters in reported results, this work evaluated the influences of layer thickness (LT), deposition speed (DS) and printing direction (PD) on tensile properties and dimensional accuracy of poly(lactic acid) 3D parts evaluating the possibility of using thin plate spline interpolation method (TPSIM) of data, a new approach, in determination of optimized fused deposition modeling process parameters. It was observed that the use of low levels of LT (0.10 mm), DS (40 mm/s), and PD (0°) provided parts with higher mechanical strength and dimensional performance. Denser parts showed lower anisotropy effect and, consequently, best tensile properties were obtained. TPSIM was an efficient mathematical analysis and well fitted results of predicted and experimental results.